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Database Design and Modeling with Google Cloud

You're reading from   Database Design and Modeling with Google Cloud Learn database design and development to take your data to applications, analytics, and AI

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781804611456
Length 234 pages
Edition 1st Edition
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Author (1):
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Abirami Sukumaran Abirami Sukumaran
Author Profile Icon Abirami Sukumaran
Abirami Sukumaran
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Database Model: Business and Technical Design Considerations
2. Chapter 1: Data, Databases, and Design FREE CHAPTER 3. Chapter 2: Handling Data on the Cloud 4. Part 2:Structured Data
5. Chapter 3: Database Modeling for Structured Data 6. Chapter 4: Setting Up a Fully Managed RDBMS 7. Chapter 5: Designing an Analytical Data Warehouse 8. Part 3:Semi-Structured, Unstructured Data, and NoSQL Design
9. Chapter 6: Designing for Semi-Structured Data 10. Chapter 7: Unstructured Data Management 11. Part 4:DevOps and Databases
12. Chapter 8: DevOps and Databases 13. Part 5:Data to AI
14. Chapter 9: Data to AI – Modeling Your Databases for Analytics and ML 15. Chapter 10: Looking Ahead – Designing for LLM Applications 16. Index 17. Other Books You May Enjoy

Modeling considerations for analytics, AI, and ML

As with relational transactional applications, analytics applications require data to be modeled, stored, and accessed to address the application’s design aspects. While the business, functional, technical, and regulatory requirements vary for each application, there are some fundamental operational and design needs that are generally considered the baseline for all analytical data modeling. We’ll look at a few of them in this section:

  • Understand the analytical requirements: Before diving into data modeling, it’s important to have a clear understanding of your analytical requirements. Define the specific questions you want to answer or the insights you want to derive from your data. This understanding will guide your data modeling efforts and help you design a database structure that aligns with your analytical goals.
  • Denormalize your data: Normalization is a widely adopted practice in traditional database...
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